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        <title>Devil is the Edges: STEAL</title>
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    <br>
    <center>
    <span style="font-size:42px">Devil is the Edges</span> 
    <br>
    <span style="font-size:36px"> Learning Semantic Boundaries from Noisy Annotations</span>
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        <span style="font-size:20px"><a href="http://www.cs.toronto.edu/~davidj/">David Acuna</a><sup> 1,2,3</sup></span>
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        <span style="font-size:20px"><a href="http://www.cs.toronto.edu/~amlan/">Amlan Kar</a><sup>  2,3</sup></span>
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            <span style="font-size:20px"><a href="http://www.cs.toronto.edu/~fidler/">Sanja Fidler</a><sup>1,2,3</sup></span>
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        <span style="font-size:20px"><sup>1</sup>NVIDIA</span>
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            <span style="font-size:20px"><sup>2</sup>University of Toronto</span>
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            <span style="font-size:20px"><sup>3</sup>Vector Institute</span>
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        <span style="font-size:20px;color:red">CVPR, 2019 <b>(Oral)</span>
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                        <br>
                        <p align="justify" style="font-size: 18px">
                            We tackle the problem of semantic boundary prediction, which aims to identify pixels that belong to object(class) boundaries. We notice that relevant datasets consist of a significant level of label noise, reflecting the fact that precise annotations are laborious to get and thus annotators trade-off quality with efficiency. We aim to learn sharp and precise semantic boundaries by explicitly reasoning about annotation noise during training. We propose a simple new layer and loss that can be used with existing learning-based boundary detectors. Our layer/loss enforces the detector to predict a maximum response along the normal direction at an edge, while also regularizing its direction.  We further reason about true object boundaries during training using a level set formulation, which allows the network to learn from misaligned labels in an end-to-end fashion. Experiments show that we improve over the CASENet backbone network by more than 4% in terms of MF(ODS) and 18.61% in terms of AP, outperforming all current state-of-the-art methods including those that deal with alignment. Furthermore, we show that our learned network can be used to significantly improve coarse segmentation labels, lending itself as an efficient way to label new data. 
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             <center><h1>News</h1></center>
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                  <ul>
                <li>[June 2019] Released Inference  <a href="https://github.com/nv-tlabs/STEAL">Code</a></li>
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                <ul>
                <li>[April 2019] Paper released on <a href="http://arxiv.org/abs/1904.07934">arXiv</a></li>
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             <center><h1>Paper</h1></center>
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                  <td><a href="./"><img style="height:180px; border: solid; border-radius:30px;" src="./resources/paper.png"/></a></td>
                  <td><span style="font-size:18px">David Acuna , Amlan Kar , Sanja Fidler<br><br>
                          Devil is in  the Edges: Learning Semantic Boundaries <br> from Noisy Annotations<br><br>
                  CVPR, 2019. (to appear)<br>
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                    <span style="font-size:18px"><center>
                      <a href="http://arxiv.org/abs/1904.07934">[Preprint]</a>
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                  <td><span style="font-size:18px"><center>
                      <a href="./resources/bibtex.txt">[Bibtex]</a>
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                  <td><span style="font-size:18px"><center>
                      <a href="./resources/steal_video.mp4">[Video]</a>
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         <center><h1>STEAL in a nutshell</h1></center>
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                          <a href='https://github.com/davidjesusacu/PolyRNN/'><img class="round" style="height:300" src="./resources/model.png"/></a>
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          <center><h1> Results</h1></center> <br>

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                        <a href="./resources/results_q.png"><img src = "./resources/results_q.png" width="900px"></img></a><br>
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                    <span style="font-size:14px">
                            Qualitative Results on the SBD Dataset
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                        <a href="./resources/results_q_cityscapes.png"><img src = "./resources/results_q_cityscapes.png" width="900px"></img></a><br>
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                    <span style="font-size:14px">
                        Qualitative Results on the Cityscapes Dataset
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          <hr>
          <center><h1>Coarse-to-fine Refinement</h1></center> 
          <center><h2>Make Better Segmentation Datasets with STEAL</h2></center> <br>

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                        <a href="./resources/coarse_to_fine_g.gif"><img src = "./resources/coarse_to_fine_g.gif" width="900px"></img></a><br>
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                    <span style="font-size:14px">
                            Qualitative Results. Coarse-to-Fine on the coarsely annotated Cityscapes train extra set.
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